Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics
Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics
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DOI：
10.1080/15732479.2016.1171883
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发表时间：
2017-05
影响因子：
3.7
通讯作者：
S. O'connor;Yilan Zhang;J. Lynch;M. Ettouney;P. O. Jansson
中科院分区：
文献类型：
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作者：
S. O'connor;Yilan Zhang;J. Lynch;M. Ettouney;P. O. Jansson
摘要：本研究的目的是推动无线传感技术永久安装在运营中的公路桥梁，以进行长期自动化健康评估。这项工作推进了太阳能无线传感器网络架构的设计，该架构可以永久部署在恶劣的冬季气候中，在那里太阳能有限，温度较低是正常的操作条件。为了展示太阳能无线传感器网络的性能，它于2011年安装在电报路（门罗，密歇根州）上承载北行I-275交通的多钢梁桥上；该桥的一个独特设计特点是使用销钉和吊架连接来支撑桥梁的主跨。安装了一个密集的应变计、加速度计和温度计网络，以获取桥梁管理者感兴趣的桥梁响应，包括可能受到桥梁长期恶化影响的响应。无线监控系统每天收集传感器数据，并将数据传输到互联网，存储在一个精心设计的数据存储库中。存储库中的桥梁响应数据被自动处理，使用机器学习提取卡车负载事件，使用非线性回归补偿环境变化，并使用统计过程控制定量评估异常桥梁性能。
Abstract The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and communicates the data to the Internet where it is stored in a curated data repository. Bridge response data in the repository are autonomously processed to extract truck load events using machine learning, compensate for environmental variations using nonlinear regression and to quantitatively assess anomalous bridge performance using statistical process control.